{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b282572e",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#  Mplfinance Used To Plot Supertrend\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f341883c",
   "metadata": {},
   "source": [
    "### What is Supertend\n",
    "- It is a trend-following indicator. The Supertrend indicator was created by Olivier Seban to work on different time frames. \n",
    "- It works for futures, forex and equities. You can run it every 15 minutes, hourly, weekly, or daily."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "265abc82",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### mplfinance 'yahoo' styles was used to customize:\n",
    "- Type of Plot Use `candle`\n",
    "- It Build As Signal Trend Line\n",
    "- Background, Grid, and Figure Colors\n",
    "- Grid style\n",
    "- Y-Axis On The Right or Left\n",
    "- Matplotlib Defaults\n",
    "- Fill Between\n",
    "- Alpha\n",
    "- Color\n",
    "#### The simplest way to do this is to choose one of the `add_plot` that come packaged with `mplfinance`\n",
    "#### but, as we see below, it is also possible to customize your own `mplfinance styles`.\n",
    "#### Also Other Plot Type Can Be Used\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "821ec5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_ta as ta\n",
    "import mplfinance as mpf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4bbf5f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32a433d",
   "metadata": {},
   "source": [
    "### Read in daily data for the S&P 500 from November of 2019 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a410f0a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-06-25</th>\n",
       "      <td>20.530001</td>\n",
       "      <td>20.83</td>\n",
       "      <td>19.99</td>\n",
       "      <td>20.040001</td>\n",
       "      <td>13.693501</td>\n",
       "      <td>61250600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-26</th>\n",
       "      <td>20.299999</td>\n",
       "      <td>20.76</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.629999</td>\n",
       "      <td>14.096654</td>\n",
       "      <td>52904900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open   High    Low      Close  Adj Close    Volume\n",
       "Date                                                               \n",
       "2003-06-25  20.530001  20.83  19.99  20.040001  13.693501  61250600\n",
       "2003-06-26  20.299999  20.76  20.15  20.629999  14.096654  52904900"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-07</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.129999</td>\n",
       "      <td>27.480000</td>\n",
       "      <td>27.620001</td>\n",
       "      <td>18.923342</td>\n",
       "      <td>72680200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-08</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.139999</td>\n",
       "      <td>27.200001</td>\n",
       "      <td>27.370001</td>\n",
       "      <td>18.752058</td>\n",
       "      <td>71791400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "2004-04-07  28.08  28.129999  27.480000  27.620001  18.923342  72680200\n",
       "2004-04-08  28.08  28.139999  27.200001  27.370001  18.752058  71791400"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf = pd.read_csv('../data/yahoofinance-INTC-19950101-20040412.csv',index_col=0,parse_dates=True).tail(200)\n",
    "\n",
    "df = idf.copy()\n",
    "df.index.name = 'Date'\n",
    "df.shape\n",
    "df.head(2)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39efb27e",
   "metadata": {},
   "source": [
    "- Supertrend indicator is plotted in stock price charts for investors and highlights prominently visible trends that are shown in red when prices have dipped and green when prices have risen. \n",
    "\n",
    "\n",
    "\n",
    "- **This indicator works on only two parameters:**\n",
    "\n",
    "- Periods: Traders usually use 10 periods – Average True Range number of days (ATR – yet another indicator that gives you market volatility value by decompressing the range of prices of a security for a particular time).\n",
    "- Multiplier: A multiplier is a value by which ATR would be multiplied. Three multipliers are used.\n",
    "- ATR plays a crucial role in this indicator since it is used to compute a value, and based on that, the degree of price volatility can be tracked. If all of these are too complex, you don’t have to worry; systems and screeners are now equipped to do the math for you in seconds – simply select the options on your computer and set up your trade strategy!\n",
    "\n",
    "- **Here is Following Calculation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1be6af19",
   "metadata": {},
   "outputs": [],
   "source": [
    "st = ta.supertrend(df['High'], df['Low'], df['Close'], length=7, multiplier=3,append=True)\n",
    "super_trend_df = df.join(st).tail(190)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84d8abff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Following Function Used To Differentiate Uptrend And Download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "54ce6e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def color(super_trend):\n",
    "    UP = []\n",
    "    DOWN = []\n",
    "    for i in range(len(super_trend)):\n",
    "        if super_trend['SUPERTl_7_3.0'][i] < super_trend['Close'][i]:\n",
    "            UP.append(float(super_trend['SUPERTl_7_3.0'][i]))\n",
    "            DOWN.append(np.nan)\n",
    "        elif super_trend['SUPERTs_7_3.0'][i] > super_trend['Close'][i]:\n",
    "            DOWN.append(float(super_trend['SUPERTs_7_3.0'][i]))\n",
    "            UP.append(np.nan)\n",
    "        else:\n",
    "            UP.append(np.nan)\n",
    "            DOWN.append(np.nan)\n",
    "    super_trend['up'] = UP\n",
    "        \n",
    "    super_trend['down'] = DOWN\n",
    "    \n",
    "    return super_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0043a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fuction Color Applied And Df Generated \n",
    "super_trend = color(super_trend_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d214d3e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "up_super_trend = super_trend[['up']]\n",
    "down_super_trend = super_trend[['down']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444b0bfb",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Let's say we want to plot the Supertrend along with the basic OHLCV plot.  \n",
    "\n",
    "We Use `make_addplot()` to create the addplot dict, and pass that into the plot() function:\n",
    "\n",
    "We Use `Color` To Define Line Colors\n",
    "\n",
    "We Use `alpha` To Define Depth Line Color\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3cc8fd1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ic = [\n",
    "    #Supertrend\n",
    "    mpf.make_addplot(up_super_trend,color = 'green',panel=0,),\n",
    "    mpf.make_addplot(down_super_trend,color = '#FF8849',panel=0,),\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4cda06",
   "metadata": {},
   "source": [
    "Use a dict to specify other attributes (kwargs) for `fill_between`:\n",
    "<br>\n",
    "To demonstrate use of the `where` kwarg to display a holding period\n",
    "<br>\n",
    "`where = supertrend['up'] > supertrend['Close']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7df8268",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill Between Method Appled\n",
    "fill_between_up=dict(y1=super_trend['up'].values,y2=super_trend['Low'].values,alpha=0.05,color='g')\n",
    "fill_between_down=dict(y1=super_trend['down'].values,y2=super_trend['High'].values,alpha=0.05,color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "236c94af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(\n",
    "    super_trend,\n",
    "    volume=True,\n",
    "    type=\"candle\", \n",
    "    style=\"yahoo\",\n",
    "    addplot=ic,\n",
    "    fill_between = [fill_between_up,fill_between_down],\n",
    "    figsize=(20,10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eb44dc8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
